This course contains the use of artificial intelligence.

Build Reliable Agentic AI Systems

Agentic AI is more than an LLM responding to a prompt. A reliable agent operates through a controlled loop: it interprets a goal, selects an action, uses a tool, observes the result, evaluates progress, and continues until it reaches a verified outcome.

This hands-on course teaches the foundations of Loop Engineering for Agentic AI. You will learn how to design, build, control, debug, and evaluate agent loops that perform meaningful work without becoming unpredictable, repetitive, or unsafe.

What You Will Build

You will build one evolving Python project throughout the course. Starting with a minimal tool-using agent, you will progressively add:

Tool calling and validated action schemas
State, memory, checkpoints, and recovery
Context-window management and compaction
Termination conditions and resource limits
Guardrails and permission boundaries
Tracing, verification, and debugging
Multi-agent orchestration and handoffs
Human approval checkpoints

The final capstone is a reliable issue-resolution agent that can inspect a repository, use development tools, preserve progress, detect non-progress, delegate verification, request approval, and produce an auditable execution report.

What You Will Learn

Explain how an agentic loop differs from a single LLM call
Design the goal–act–observe–evaluate cycle
Build a working tool-calling agent loop in Python
Create clear tool contracts and validate agent actions
Handle tool errors, retries, timeouts, and invalid requests
Manage state and memory across agent iterations
Checkpoint, resume, and recover interrupted agent runs
Control context growth and prevent context drift
Define reliable success, failure, blocked, and escalation outcomes
Detect repetition, oscillation, and non-progress
Apply permissions, guardrails, and risk-based approvals
Verify outcomes using tests, validators, and reviewer agents
Trace, replay, diagnose, and repair failed runs
Implement sub-agent, orchestrator-worker, and handoff patterns
Apply Loop Engineering concepts with Claude Code
Prepare agentic systems for safe production use

Claude Code and Multi-Agent Workflows

A dedicated section demonstrates how Claude Code can support Loop Engineering through project instructions, skills, plugins, MCP integrations, hooks, automations, specialized sub-agents, permissions, and Git worktrees.

You will compare single-agent and multi-agent designs, implement an orchestrator-worker workflow, define reliable handoff contracts, and prevent delegation loops or conflicting work.

Hands-On Course Format

Concise, focused theory
Progressive guided labs
Python coding exercises
Decision-based role-play activities
Section quizzes
Two full-length practice tests
Reusable templates and checklists
One integrated capstone project

A mock LLM adapter supports no-cost practice. An optional live-model adapter is included for learners who want to experiment with a real model provider.

Who This Course Is For

AI engineers building agentic applications
Software developers moving beyond basic prompting
Solution architects designing reliable AI systems
Technical leads evaluating agent architectures
Automation engineers creating tool-driven workflows
Learners interested in Claude Code and multi-agent development

Basic Python knowledge is helpful, but prior experience building AI agents is not required.

Course Outcome

By the end of the course, you will understand not only how to make an agent act, but also how to make it stop correctly, recover safely, verify completion, escalate intelligently, and remain under meaningful human control.